The evolutionary approach is a family of probabilistic search algorithms. The genetic algorithm (GA) and particle swarm optimization (PSO) are members of the evolutionary family, where both GA and PSO have been proven to be successful in finding good solutions in a short time for many combinatorial problems. In this paper, we have proposed several metrics, in the form of distance functions (DP), to examine and compare the internal behavior of GA and PSO based on a problem-specific DF rather than an algorithmic DF. Our initial experimental results show that PSO has more smooth and steady distance function values than GA.